Method and apparatus for wirelessly monitoring repetitive bodily movements

ABSTRACT

A method for determining a rate of repetitive bodily motion of an individual with negligible contact with the individual begins by one or more computing devices transmitting a signal for reflection off of the individual, receiving a reflected signal, and down-converting the reflected signal to a baseband signal. The method continues with one or more computing device applying a frequency estimation algorithm to the baseband signal to produce an estimated spectral density, and applying a repetitive bodily motion pattern search function to the estimated spectral density to estimate the rate of the repetitive bodily motion of the individual.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/243,255,entitled “METHOD AND APPARATUS FOR WIRELESSLY MONITORING REPETITIVEBODILY MOVEMENTS”, filed Oct. 19, 2015, which is hereby incorporatedherein by reference in its entirety and made part of the present U.S.Utility patent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to wireless communication and moreparticularly to a method and apparatus for wirelessly monitoringrepetitive bodily movements.

Description of Related Art

Laser (Light Amplification by Stimulated Emission of Radiation) systemsare known for their use in wireless data gathering applications. Forexample, a laser-measuring tool, such as a laser rangefinder, uses alaser beam to determine the distance to and/or from an object. Asanother example, a laser is used for measurement based on the“time-of-flight” principle, which refers to sending a laser pulse in anarrow beam towards an object, measuring the time taken by the pulse tobe reflected off the target, and calculating the distance based on thetime. Other laser data gather applications include triangulation,interferometers, phase shift methods, and temperature measurements.Lasers can be used in three-dimensional (3D) object recognition, 3Dmodeling, and a wide variety of computer vision related fields.

Similarly, radio or microwave frequency signals are known for their usein wireless measurements. For example, radar systems are known objectdetection systems that use radio or microwaves to determine the range,velocity, or angle of objects. Radar systems use electromagnetic wavesto measure distances. Common techniques for measuring distances usingelectromagnetic waves include time-of-flight, frequency modulation, andphased array method.

Radio and microwave frequency signals are further known for their use inmotion detection. A tomographic motion detector uses a mesh system ofradio frequency (RF) nodes. Changes in the baseline signal strengthbetween nodes indicate a human presence or motion. This principle istypically implemented using signals in the 2.4 GHz range. A microwavebased motion detector operates through the principle of Doppler radar. Acontinuous wave of microwave radiation is emitted (typically in therange of 915 MHz) and any phase shifts in the reflected microwave due tomotion of an object are received as a heterodyne signal at low audiofrequencies.

Airport security checkpoints and other security screening locations haveimplemented full body scanners to wirelessly detect concealed objectsunder a person's clothing. Whole body scanning is implemented throughthe use of backscatter X-ray, active millimeter wave, or passivemillimeter wave technology. Backscatter X-ray scanners use weak X-raysto detect radiation that reflects from an object to form an image.Images are taken from both sides of the body to create a two-dimensional(2-D) image of the person and anything else on that person's body.Active millimeter wave scanners direct millimeter wave energy at theperson and interpret the reflected energy. Because clothing and manyother materials are translucent to extremely high frequency bands (EHF)such as the 24-30 GHz band emitted by millimeter-wave scanners, the waveenergy reflected back from the body or other objects on the body is usedto construct a 3D image, which can be displayed for analysis. Incontrast, passive millimeter wave scanners create images using ambientradiation and radiation emitted from the human body or objects.

Bluetooth is a known wireless technology for exchanging data over shortdistances from fixed and/or mobile devices and building personal areanetworks (PANs). Bluetooth technology uses short wavelength, ultra highfrequency (UHF) radio waves in the industrial, scientific, and medical(ISM) radio band from 2.4 to 2.48 GHz to establish wireless connectionsbetween devices.

Currently, “wireless” heart rate monitors include a sensor that makescontact with the body (e.g., a sensor attached to the wrist, across thechest, etc.) that wirelessly communicates with another device (e.g., asmart phone, watch, etc.). As such, wireless heart rate monitors requirecontact with the human body to sense the appropriate data.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a wirelessmonitoring repetitive bodily movement system in accordance with thepresent invention;

FIGS. 2 and 3 are diagrams of an example of human respiration and heartrate;

FIG. 4 is a schematic block diagram of an embodiment of a transceiversection of the wireless monitoring repetitive bodily movement system inaccordance with the present invention;

FIG. 5 is a schematic block diagram of an embodiment of a processingmodule of the wireless monitoring repetitive bodily movement system inaccordance with the present invention;

FIG. 6 is a schematic block diagram of another embodiment of aprocessing module of the wireless monitoring repetitive bodily movementsystem in accordance with the present invention;

FIG. 7 is a schematic block diagram of an embodiment of a wirelessmonitoring repetitive bodily movement system in accordance with thepresent invention;

FIG. 8 is a flowchart illustrating an example of determining a rate ofrepetitive bodily motion in accordance with the present invention;

FIG. 9 is a flowchart illustrating an example of applying a frequencyestimation algorithm in accordance with the present invention;

FIG. 10 is a flowchart illustrating an example of applying a repetitivebodily motion pattern search function in accordance with the presentinvention;

FIG. 11A is a flowchart illustrating another example of applying therepetitive bodily motion pattern search function in accordance with thepresent invention;

FIG. 11B is a diagram of an example of human respiration and heart ratefrequencies;

FIG. 12 is a flowchart illustrating another example of applying therepetitive bodily motion pattern search function in accordance with thepresent invention;

FIG. 13 is a flowchart illustrating another example of applying therepetitive bodily motion pattern search function in accordance with thepresent invention; and

FIG. 14 is a schematic block diagram of an embodiment of a processingmodule of the wireless monitoring repetitive bodily movement system inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a wirelessmonitoring repetitive bodily movement system 10 (“monitoring system”),which may be implemented via one or more computing devices. Themonitoring system 10 includes a signal generator 12, a transceiversection 26, an analog to digital converter (ADC) 20, memory 24, and aprocessing module 22. The transceiver section 26 includes a transmittersection 14, a local oscillation generator (LO GEN) 16, a receiversection 18, and antennas.

A computing device may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core (e.g., includesone or more of main memory, a processing module, a memory controller,input/output controller, external memory interface, and peripheraldevice controller). A fixed computing device may be a personal computer(PC), a computer server, a cable set-top box, a satellite receiver, atelevision set, a printer, a fax machine, home entertainment equipment,a video game console, and/or any type of home or office computingequipment that includes a computing core.

In an example of operation, the signal generator 12 generates acontinuous wave reference signal (e.g., a 30 kHz sinusoidal signal) fortransmission. The transmitter section 14 up-converts the continuous wavereference signal to a radio frequency signal (e.g., a 2.4 GHz signal)and transmits the signal (e.g., radio frequency (RF) signal 28) forreflection off of an individual. The individual could include a human,animal, reptile, or any being capable of producing a repetitive bodilymotion. The repetitive bodily motion may include heartbeat, respiration,eye movement, spasms, ticks, or any other repetitive bodily movement.

The receiver section 18 receives a reflected signal 30, which iscorresponds to the transmitted RF signal 28 being reflected and/orrefracted off of the individual. The reflected signal 30 includes one ormore of the transmitted signal 28, a clutter signal component (e.g.,reflection of the transmit signal 28 off of objects other than theindividual), a multipath signal component (e.g., non-direct pathreflections of the transmitted signal off of the individual), a noisesignal component, and a Doppler shifted version of a repetitive bodilymotion of the individual (e.g., the desired signal). For example, thechest wall and the heart wall oscillate and have zero net velocity. TheDoppler shift due to the heartbeat and respiration movement of the chestcan be expressed by phase modulation of the reflected signal 30. Assuch, the reflected signal 30 includes Doppler shifted version of therepetitive bodily motion (e.g., heartbeat and/or respiration movementsof the chest).

The receiver section 18 down-converts the reflected signal 30 to anintermediate frequency (IF) (e.g., 30 KHz). The ADC 20 converts theanalog IF signal into a digital IF signal. As will be described ingreater detail with reference to one or more of FIGS. 5-14, theprocessing module 22 down converts the IF signal to a baseband signal(e.g., removes the 30 KHz signal component). The processing module 22processes the baseband signal to remove or substantially reduce theclutter signal component, the multipath signal component, and the noisesignal component of the baseband signal, leaving the desired Dopplershift signal component(s) of the repetitive bodily motion.

In order to isolate the Doppler shifted signal components of the signal,the processing module 22 applies a frequency estimation algorithm to thebaseband signal to produce an estimated spectral density. The processingmodule 22 then applies a repetitive bodily motion pattern searchfunction to the estimated spectral density to detect the Doppler shiftsand estimate the rate of the repetitive bodily motion of the individualwith negligible contact with the individual. Knowing that the Dopplershifted version of the repetitive bodily motion (e.g., the heart rateand/or respiration) are within certain frequency ranges help fine tunethe repetitive bodily motion pattern search function. The monitoringsystem 10 is further operable to output the rate of the detectedrepetitive bodily motion of the individual for display and/or furtheranalysis.

FIGS. 2 and 3 are diagrams of an example of the repetitive bodilymotions of human respiration and heart rate. FIG. 2 depicts an exampleof chest displacement caused during respiration. When exhaling 32, thechest falls and upon inhaling 34, the chest rises. This respirationdisplacement 36 may be a few centimeters or more. Similarly, themovement of a heartbeat causes a small displacement of the chest of afew millimeters or more. FIG. 3 depicts heartbeat displacement 38 andrespiration displacement 36 as displacement over time. Heartbeatdisplacement 38 has a higher frequency but smaller amplitude incomparison to the respiration displacement 36. Known properties of thesesignals can aid in the detection and tuning of the Doppler shiftedversions of repetitive bodily motion present in the reflected signal.

FIG. 4 is a schematic block diagram of an embodiment of the transceiversection 26 of the monitoring system 10 that includes the transmittersection 14 and the receiver section 18. The transmitter section 14includes IQ phase shift modules (0°,90°), mixers, power amplifiers (PA),a transformer balun (xfmr balun), and an antenna. The receiver section18 includes an antenna, a transformer balun, low noise amplifiers,mixers, an IQ phase shift module, low pass filters (LPF) 40, and highpass filter DC cancelation modules 42.

In an example of operation, the signal generator 12 generates acontinuous wave reference signal (e.g., a 30 KHz continuous wavesinusoidal reference signal). An IQ phase shift module generates anin-phase reference signal (0° phase shift) and a quadrature referencesignal (90° phase shift). The local oscillator generator (LO GEN) 16generates a transmitter local oscillation (e.g., 2.4 GHz) and a receivelocal oscillation (e.g., 2.4 GHz). Note that the transmit and receivelocation oscillations may be separate signals or the same signal.

A second IQ phase shift module generates an in-phase local oscillationsignal (0° phase shift) and a quadrature local oscillation signal (90°phase shift) from the transmit local oscillation. A first mixer mixesin-phase reference signal (e.g., sin Φ(t)) with the in-phase localoscillation (e.g., sin ω_(LO)(t)) to produce a first up-converted signalcomponent (e.g., ½*cos(ω_(LO)(t)−Φ(t))−½*cos(ω_(LO)(t)+Φ(t))). A secondmixer mixes the quadrature reference signal (e.g., cos Φ(t)) with thequadrature local oscillation signal (e.g., cos ω_(LO)(t)) to produce asecond up-converted signal component (e.g.,½*cos(ω_(LO)(t)−Φ(t))+½*cos(ω_(LO)(t)+Φ(t))).

The power amplifiers amplify the first and second up-converted signals,which are summed via the transformer balun to produce the transmit RFsignal 28 (e.g., cos ω_(RF)(t)=cos(ω_(LO)(t)+Φ(t))). Alternatively, thefirst and second up-converted may be summed prior to amplification by apower amplifier. Note that one or more RF bandpass filters may beincluded in prior to and/or after the power amplifiers.

The resulting RF signal 28 is transmitted via the antenna. The antennamay be a single omnidirectional antenna, a single directional antenna,and/or two or more diversity antennas. The antenna may further includebeamforming to focus the transmitted RF signal.

The radio frequency signal 28 is reflected off of an individual andreceived by the antenna of the receiver section 18 as the reflectedsignal 30. The reflected signal 30 includes a Doppler shifted version ofthe repetitive bodily motion, a clutter signal component (e.g.,reflection of the RF signal 28 off of other objects), a multipath signalcomponent (e.g., indirect signal paths of the RF signal reflecting offof the individual), a transmit-receive coupled signal component (e.g.,reception of the RF signal 28 via cross coupling within thetransceiver), and a noise component (e.g., phase noise of the LO GEN).

The clutter, multipath, transmit-receive coupling, and the noise signalcomponents are in-band interferers with the desired Doppler shiftedversion of the repetitive bodily motion signal component. In particular,the LO GEN 16 does not generate a clean sinusoidal signal but rather itcreates one with a time varying random phase noise, θ(t). Hence thepractical transmit local oscillation is, s(t)=sin(2πf_(c)t+θ(t)) (wheref_(c) is the frequency, e.g., 2.4 GHz). Therefore, the spectrum of s(t)is not a delta function as in the ideal case, but has an extended skirtdue to phase noise. When this signal under goes a Doppler shift due to arepetitive bodily motion, the complete spectra of the signal is shiftedas illustrated in the bottom left of FIG. 4. The Doppler shift signalcomponents (e.g., f_(rf-res) and f_(rf-hr), where “res” corresponds torespiration and “hr” corresponds to heart rate) are small and fall inthe region of this skirt. The clutter, multipath, transmit-receivecoupling signal components further contribute to the skirt.

In an example of operation, the receiver section 18 receives thereflected signal 30. The transformer balun converts the single endedsignal reflected signal 30 into a differentiated signal (e.g., anin-phase reflected signal and a quadrature reflected signal). Thein-phase reflected signal and a quadrature reflected signal are eachamplified by low noise amplifiers (LNA). A first mixer mixes theamplified in-phase reflected signal (e.g., sin ω_(RF)(t)) with anin-phase receive local oscillation (e.g., sin ω_(LO)(t)) from LO GEN 16and a second mixer mixes the amplified quadrature reflected signal(e.g., cos ω_(RF)(t)) with a quadrature receive local oscillation (e.g.,cos ω_(LO)(t)).

Low pass filters 40 filter the mixed in-phase signal (e.g.,½*cos(ω_(RF)(t)−ω_(LO)(t))−½*cos(ω_(RF)(t)+ω_(LO)(t))) and mixedquadrature signal (e.g.,½*cos(ω_(RF)(t)−ω_(LO)(t))−½*cos(ω_(RF)(t)+ω_(LO)(t))) to produce adown-converted in-phase intermediate frequency (IF) signal (e.g.,½*cos(ω_(RF)(t)−ω_(LO)(t))=½*cos(ω_(iF)(t)), where ω_(iF)(t) has afrequency corresponding to the frequency of the reference signal Φ(t))and a down-converted quadrature IF signal (e.g.,½*cos(ω_(RF)(t)−ω_(LO)(t))=½*cos(ω_(iF)(t)). The high pass filter DCcancelation filters 42 filter the down-converted in-phase IF signal andthe down-converted quadrature IF signal to substantially attenuatesignal components below the intermediate frequency (e.g., 30 KHz). TheADC 20 converts the down-converted in-phase (I) and quadrature (Q) IFsignals into digital I and Q IF signals.

FIG. 5 is a schematic block diagram of an embodiment of the processingmodule 22 of the monitoring system 10. The processing module 22 isconfigured to include a down convert module 44, a cross coupling filter46, a clutter and multi-path noise filter 48, a Doppler phase shiftdetection module 50, and a repetitive bodily movement rate estimationmodule 52 (e.g., shown here as a heart and respiration rate estimationmodule).

In an example of operation, the processing module 22 receives thedigital I and Q IF signals (hereinafter referred to as IF signal) fromthe ADC 20. The IF signal includes an IF carrier frequency component,the desired Doppler shifted version of the repetitive bodily motionssignal component (e.g., reflection off chest (rch) and reflection offheart (rh)), the TX-RX cross coupling signal component, and clutter andmultipath noise signal component (rcmp). The down convert module 44 downconverts the IF signal to a baseband signal by removing the IF carrierfrequency component. The cross coupling filter 46 substantially removesthe TX-RX cross coupling signal component from the baseband signal. Theclutter and multipath noise filter 48 removes the clutter and noisesignal components from the baseband signal. The Doppler phase shiftdetection module 50 searches the baseband signal for Doppler shiftedversion of repetitive bodily motions signal components. The repetitivebodily movement rate estimation module 52 interprets the Doppler phaseshift signal components to estimate the repetitive bodily movement(e.g., the heart and respiration rate estimation module). For example,based on an expected range of frequencies for heart rate (e.g., 60-80beats per minute) and respiration (e.g., 16-20 breathes per minute), therepetitive bodily movement rate estimation module 52 identifies theheart rate and the respiration rate from the Doppler phase shiftinformation corresponding to those frequencies.

FIG. 6 is a schematic block diagram of another embodiment of theprocessing module 22 of the monitoring system 10. The processing module22 is implemented to include a filter and down convert module 54, anestimate covariance matrix module 56, an estimate clutter covariancematrix module 58, an adder, a frequency estimation algorithm module 60,and a repetitive bodily motion pattern search function module 62.

In an example of operation, the filter and down convert module 54receives the sampled, down converted IF signal (e.g., r_(IF)[k]) fromthe transceiver section. The filter and down convert module 54down-converts the sampled IF signal to a baseband signal. For example,the module 54 low pass filters the IF signal to a stop frequency (e.g.,of 50 Hz) and then down samples the filtered signal such that the newsampling frequency is 50 Hz. This process removes the intermediatefrequency (e.g., 30 KHz) from the signal and shifts the signalcomponents closer to 0 Hz to create a filtered and down-sampled basebandsignal at DC (e.g., r[k]).

In order to remove the TX-RX cross coupling and cancel the clutter andmulti path noise from the signal, the estimate covariance matrix module56 estimates the signal's covariance matrix over a window of samples andaverages it over multiple overlapping windows. To estimate thecovariance matrix of the signal, the estimate covariance matrix module56 captures the transmitted signal through shorting the transmit andreceive paths to estimate the covariance of the transmitted signal. Thetransmitted signal and the noise (e.g., Additive White Gaussian Noise(AWGN)) components of the received signal are assumed to be random,independent variables. Next, K number of samples of the received signalis windowed. The covariance matrix can then be calculated (as a functionof the random variables, the covariance matrix of the transmittedsignal, the variance of the AWGN, and the multi path components andtheir respective Doppler shifts) over this window. The covariance matrixof the baseband signal (e.g., Re) is averaged by sliding the window overthe entire duration of the captured signal.

Next, the estimate clutter covariance matrix 58 estimates the basebandsignal's clutter covariance matrix. The estimate clutter covariancematrix 58 first records a received signal without an individual in frontof the monitoring system 10 to estimate the clutter multi pathreflection due to ambiance (e.g., a loop back measurement). Then theestimate clutter covariance matrix 58 calculates the covariance matrixof the clutter signal as a function of the random variables, thecovariance of the transmitted signal, the variance of the AWGN, and themulti path components and their respective Doppler shifts. The cluttercovariance matrix (e.g., Rcc) is then scaled by the ratio of the TX/RXcoupling power (e.g., |α_(c)|²) determined during the loop backmeasurement. The baseband covariance matrix is then subtracted by theclutter covariance matrix to produce a resultant matrix (e.g., R′_(n′)).By subtracting the clutter covariance matrix from the covariance matrixafter scaling it with the appropriate scalar, the clutter can becanceled and the SNR (signal to noise ratio) of repetitive bodily motionsignal (e.g., heart rate signal, respiration signal, etc.) can beincreased.

With the clutter from the baseband signal now canceled (i.e.,significantly attenuated to render the clutter signal componentinsignificant), the frequency estimation algorithm module 60 applies afrequency estimation algorithm to the resultant matrix (R′_(n′)) toproduce an estimated spectral density (P(f)) in order to accuratelyestimate closely spaced signals in frequency domain. A frequencyestimation algorithm such as Multiple Signal Classification (MUSIC) isable to estimate the pseudo spectrum of a signal or a correlation matrixusing an eigen space analysis method. For instance, the MUSIC algorithmdetects frequencies in a signal by performing an eigen decomposition onthe resultant matrix of the baseband signal. From the eigendecomposition of the baseband resultant matrix, the eigen vectorsassociated with the N maximum eigenvalues are used to define the signalsubspace and other eigenvectors are used to define the noise subspace.From the orthogonality of the signal and noise subspaces, the peaks canbe found in the estimator function 60. These peaks represent theestimated spectral density (P(f)) of the signal.

The estimated spectral density (P(f)) is then used by the repetitivebodily motion pattern search function module 62 to identify componentsthat are indicative of repetitive bodily motion. For example, aheartbeat and respiration search function is applied to the estimatedspectral density to search for and determine principal components ofheartbeat and respiration. Then the heart rate and a respiration ratecan be estimated from the determined principal components. As anotherexample, an eye movement search function can be applied to the estimatedspectral density to determine principal components of eye movement,wherein the eye movement corresponds to the repetitive bodily motion.Based on the determined principal components of eye movement, the eyemovement rate can be estimated.

FIG. 7 is a schematic block diagram of another embodiment of thewireless monitoring repetitive bodily movement system 10. The wirelessmonitoring repetitive bodily movement system 10 includes computingdevice 1 and computing device 2. Computing device 1 includes a signalgenerator 12, a transceiver section 26, an analog to digital converter(ADC) 20, and memory 24. The transceiver section 26 includes atransmitter section 14, a local oscillation generator (LO GEN) 16, areceiver section 18, and antennas. Computing device 2 includesprocessing module 22.

In an example of operation, the transmitting the signal for reflectionoff of the individual is performed by a first computing device (e.g.,computing device 1) of one or more computing devices. The signalgenerator 12 generates a continuous wave reference signal (e.g., a 30kHz sinusoidal signal) for transmission. The transmitter section 14up-converts the continuous wave reference signal to a radio frequencysignal and transmits the signal (e.g., radio frequency (RF) signal 28)for reflection off of the individual.

The receiver section 18 receives the reflected signal 30, wherein thereflected signal 30 includes one or more of the transmitted signal, aclutter signal component, a multipath signal component, a noisecomponent, and a Doppler shifted version of a repetitive bodily motionof the individual. The repetitive bodily motion may include heartbeat,respiration, eye movement, spasms, ticks, or any other repetitive bodilymovement. For example, the reflected signal could contain Dopplershifted versions of the heartbeat and respiration movement of the chest.The receiver section 18 and the ADC 20 sample and down-convert thereflected signal 30 to an IF signal 64.

Computing device 2 receives the baseband signal 64 via a wired and/orwireless connection to computing device 1. The processing module 22applies the frequency estimation algorithm to the baseband signal toproduce the estimated spectral density. The processing module 22 thenapplies a repetitive bodily motion pattern search function to theestimated spectral density to estimate the rate of the repetitive bodilymotion of the individual with negligible contact with the individual.Knowing that the Doppler shifted version of the repetitive bodily motion(e.g., the heart rate and respiration) are within certain frequencyranges help fine tune the repetitive bodily motion pattern searchfunction.

FIG. 8 is a flowchart illustrating an example of determining a rate ofrepetitive bodily motion. The method for determining a rate ofrepetitive bodily motion of an individual with negligible contact withthe individual, is executable by one or more computing devices andbegins with step 68 where a signal is transmitted for reflection off ofan individual. An individual may include a human, animal, reptile, orany being capable of producing a repetitive bodily motion. Therepetitive bodily motion includes at least one of heartbeat,respiration, eye movement, spasms, and ticks. Transmitting the signalincludes the steps of generating a continuous wave reference signal,up-converting the continuous wave reference signal to a radio frequencysignal, and transmitting the radio frequency signal as the signal forreflection off the individual.

The method continues with step 70 where a reflected signal is received.The reflected signal includes one or more signal components includingthe transmitted signal, a clutter signal, a multipath signal, a noisecomponent, and a Doppler shifted version of the repetitive bodilymotion. The reflected signal may be a reflection and/or a refraction ofthe transmitted off of the individual.

The method continues with step 72 where the received signal is downconverted to a baseband signal. The received signal is firstdown-converted to an IF signal at an intermediate frequency (e.g., 30KHz) and over-sampled. The down-converted, oversampled signal is thenfurther processed to remove the intermediate frequency thereby producinga baseband signal.

The method continues with step 74 where a frequency estimation algorithm(e.g., MUSIC) is applied to the baseband signal to produce an estimatedspectral density. In order to apply the frequency estimation algorithm,the baseband signal is further processed to remove (i.e., substantiallyattenuate) the clutter signal, multipath signal, and noise componentfrom the baseband signal. These components are separated and removedfrom the signal so that the components that are indicative of therepetitive bodily movement (e.g., the Doppler shifted version of therepetitive bodily motion) may be isolated. Once these components areisolated, the frequency estimation algorithm (e.g., MUSIC) is applied tothe baseband signal to produce the estimated spectral density. Anexample of applying the frequency estimation algorithm is furtherdiscussed with reference to FIG. 9.

The method continues with step 76 where a repetitive bodily motionpattern search function is applied to the estimated spectral density toestimate the rate of the repetitive bodily motion of the individual. Anexample of applying the repetitive bodily motion pattern search functionis further discussed with reference to FIG. 10. The rate of therepetitive bodily motion can then be outputted for display and/orfurther analysis.

FIG. 9 is a flowchart illustrating an example of applying a frequencyestimation algorithm. The method begins with step 78 where thecovariance matrix of the baseband signal is estimated. In order toremove the TX-RX cross coupling and cancel the clutter and multi pathnoise from the baseband signal so that the frequency estimationalgorithm can be applied, the baseband signal's covariance matrix isestimated over a window of samples and averaged over multipleoverlapping windows. To estimate the covariance matrix of the signal,the transmitted signal is captured by shorting the transmit and receivepaths to estimate the covariance of the transmitted signal. Thetransmitted signal and the noise (e.g., Additive White Gaussian Noise(AWGN)) components of the baseband signal are assumed to be random,independent variables. Next, K number of samples of the received signalis windowed. The covariance matrix can then be calculated (as a functionof the random variables, the covariance matrix of the transmittedsignal, the variance of the AWGN, and the multi path components andtheir respective Doppler shifts) over this window. The covariance matrixof the baseband signal is averaged by sliding the window over the entireduration of the captured signal.

The method continues with step 80 where the clutter covariance matrix ofthe baseband signal is estimated. The clutter covariance matrix isestimated by first recording the received signal without an individualin front of the device to estimate the clutter multi path reflection dueto ambiance (e.g., a loop back measurement). The clutter signal can thenbe estimated as a function of the random variables, the covariance ofthe transmitted signal, the variance of the AWGN, and the multi pathcomponents and their respective Doppler shifts. The clutter covariancematrix is then scaled by the ratio of the TX/RX coupling powerdetermined during the loop back measurement.

The method continues with step 82 where the resultant matrix isgenerated from the clutter covariance matrix and the covariance matrixof the baseband signal. By subtracting the clutter covariance matrixfrom the covariance matrix after scaling it with the appropriate scalar,the clutter can be canceled and the SNR of repetitive bodily motionsignal (e.g., heart rate signal) can be increased.

The method continues with step 84 where the frequency estimationalgorithm is applied to the resultant matrix to produce the estimatedspectral density. With the clutter signal, multipath signal, and noisecomponents from the baseband signal now canceled, the frequencyestimation algorithm such as Multiple Signal Classification (MUSIC) isapplied. The frequency estimation algorithm such as MUSIC is able toestimate the pseudospectrum of a signal or a correlation matrix using aneigenspace analysis method. For instance, the MUSIC algorithm detectsfrequencies in the baseband signal by performing an eigen decompositionon the resultant matrix of the baseband signal. From the eigendecomposition of the baseband resultant matrix, the eigenvectorsassociated with the N maximum eigenvalues are used to define the signalsubspace and other eigenvectors are used to define the noise subspace.From the orthogonality of the signal and noise subspaces, the peaks canbe found in the estimator function. These peaks represent the estimatedspectral density of the signal.

FIG. 10 is a flowchart illustrating an example of applying a repetitivebodily motion pattern search function. The method begins with step 86where the repetitive bodily motion pattern search function is applied tothe estimated spectral density of the baseband signal. As an example, aheartbeat and respiration search function is applied to the estimatedspectral density. As another example, an eye movement search functioncan be applied to the estimated spectral density.

The method continues with step 88 where principal components of therepetitive bodily motion are determined. For example, the heartbeat andrespiration search function is applied to the estimated spectral densityto search for and determine principal components of heartbeat andrespiration. As another example, the eye movement search function can beapplied to the estimated spectral density to determine principalcomponents of eye movement, wherein the eye movement corresponds to therepetitive bodily motion.

The method continues with step 90 where the rate of the repetitivebodily motion is estimated based on the determined principal components.For example, the determined principal components of heartbeat andrespiration are used to estimate heart rate and respiration rate. Asanother example, the determined principal components of eye movement areused to estimate the rate of eye movement.

FIG. 11A is a flowchart illustrating another example of applying therepetitive bodily motion pattern search function. The method begins withset step 92 where the characteristics (e.g., biometrics) of anindividual are determined at rest. Biometrics of the individual mayinclude age, sex, size, and physical condition (e.g., athlete, sedentarylifestyle, moderately active lifestyle, etc.).

The method continues with step 94 where an expected range forrespiration rate (RR) and heartbeat rate (HR) signals are determinedbased on those characteristics. Depending on the biometrics of theindividual, the expected range of the respiration rate and heartbeatrate can differ. For example, a male athlete between the ages of 18-25should have an average resting heart rate of about 49-55 beats perminute (BPM) whereas a male in the same age range with average physicalcondition should have an average resting heart rate of about 70-73 BPM.A female athlete between the ages of 18-25 should have an averageresting heart rate of about 54-60 BPM whereas a female in the same agerange with average physical condition should have an average restingheart rate of about 74-78 BPM. Further, a male athlete over the age of65 should have an average resting heart rate of about 50-55 BPM and afemale athlete over the age of 65 should have an average resting heartrate of about 54-59 BPM.

In general, well-conditioned athletes will typically have a restingheart rate ranging from 40-60 BPM. Children over the age of 10 andadults will typically have a resting heart rate ranging from 60-100 BPM.Children between the ages of 1-10 will typically have a resting heartrate ranging from 70-130 BPM. Infants between the ages of 6-12 monthsold will typically have a resting heart rate ranging from 100-160 BPM.Infants between the ages of 3-6 months old will typically have a restingheart rate ranging from 90-120 BPM and newborns (0-3 months) willtypically have a resting heart rate ranging from 100-150 BPM.

Likewise, respiration rates differ based on age, sex, and physicalcondition. Age plays a primary role in differing respiration rates. Forexample, newborns between the ages of 0-6 weeks have a respiration rateof 30-60 breaths per min. Infants at 6 months old have a respirationrate of 25-40 breaths per min. Children at 3 years old have arespiration rate of 20-30 breaths per min. Children at 6 years old havea respiration rate of 18-25 breaths per min. Children at 10 years oldhave a respiration rate of 12-15 breaths per min. Adults generally havea respiration rate of 16-20 breaths per min. Elderly persons over theage of 65 typically have a respiration rate of 12-28 breaths per minute.Elderly persons over the age of 80 typically have a respiration rate of10-30 breaths per minute.

The method continues with step 96 where the pattern search function isadjusted to search within the expected ranges for respiration rate andheartbeat rate. The parameters of the frequency estimation algorithm mayalso be adjusted depending on the expected range of frequencies. Forinstance, if the individual is an adult male with average physicalcondition his heart rate is most likely between 70-73 beats per minuteand his respiration rate is somewhere between 16-20 breaths per min. Thepattern search function would be adjusted to search the basebandsignal's estimated spectral density for frequencies representative ofthose specified ranges.

The method continues with step 98 where it is determined whether signalsare found within the expected range. If no signals are located withinthe expected ranges, the method continues with step 100 where theexpected ranges are adjusted to broaden the search. For instance, eventhough the individual being measured in this example (the adult malewith average physical condition) has an average heart rate of 70-73beats per minute and an average respiration rate between 16-20 breathsper min, other factors such as stress, injury, anxiety, diet, andfatigue can put actual heartbeat and respiration rates outside of theexpected range. For instance, if the individual is slightly nervous hisheart rate may in fact be at 80 beats per minutes instead of within theexpected range of 70-73 beats per min. When the signal is not found inthis expected range, the search function will adjust to search outsideof the expected range until a signal is found. For example, at step 100,the expected range (e.g., 70-73 beats per min) is adjusted (e.g., to68-75 beats per min) to broaden the search.

The method would then continue to step 96 where the pattern searchfunction to search within the expected ranges for heartbeat rate andrespiration rate are adjusted. In this example, the signal would stillnot be found at step 98 until the expected ranges are adjusted toinclude a heartbeat rate of 80 beats per min. When the signals are foundat step 98, the method continues with step 102 where the estimatedrespiration rate and heartbeat signal found within the expected rangesare output for display and/or further analysis.

FIG. 11B is a diagram of an example of human respiration and heart ratefrequencies. Graph 104 depicts the respiration frequency signal andheart rate frequency signal for an infant. The respiration frequencysignal is within the expected respiration rate range for an infant 106and the heart rate frequency signal is within the expected heart raterange for an infant 108.

Graph 110 depicts the respiration frequency signal and heart ratefrequency signal for a fit adult. The respiration frequency signal iswithin the expected respiration rate range for a fit adult 112 and theheart rate frequency signal is within the expected heart rate range fora fit adult 114.

The frequency ranges of the fit adult's respiration rate and heart rateare lower than that of the infant. In fact, the expected heart raterange for the fit adult 114 falls into the expected respiration raterange for the infant 106. For example, the expected respiration raterange for the infant 106 may be 30-60 breaths per minute while theexpected heart rate range for the fit adult 114 may be between 40-60beats per minute. Specifying the type of individual (e.g., fit adult oran infant) will help tune filtering for the pattern search function.Also, for an individual, heart rate will be higher in frequency andlower in amplitude than the respiration rate. These known qualities helpto fine tune the pattern search function.

FIG. 12 is a flowchart illustrating another example of applying therepetitive bodily motion pattern search function. The method begins withstep 116 where biometrics of an individual at rest are obtained.Biometrics of the individual may include age, sex, size, and physicalcondition (e.g., athlete, sedentary lifestyle, moderately activelifestyle, etc.).

The method continues with step 118 where the expected range forrespiration rate and heart rate signals are determined based on theindividual's biometrics. The method continues with step 120 where thepattern search function is adjusted to search within the expectedranges. If the respiration rate signal and heart rate signal are foundwithin these expected ranges, the method continues to step 128 where theestimated respiration rate and heart rate are output for display and/orfurther analysis.

If the respiration rate is not found, the method continues to step 122where the respiration rate range is adjusted to broaden the searchrange. The pattern search function would then be adjusted at step 120 tosearch within the updated expected ranges. If the respiration ratesignal is detected but the heart rate signal is not found, the methodcontinues to step 124 where the expected range for the heart rate isadjusted based on the found respiration signal. For example, therespiration rate is typically between ½ to ⅕ that of the heart rate. Forinstance, if the respiration rate is found to be 16 breaths per minute,the heart rate is most likely between 32 and 80 beats per minute. Assuch, with the respiration rate known, the range for the heart rate canbe fine tuned to search within a range based on the respiration rate.

The method continues with step 126 where the pattern search function isadjusted within the adjusted expected heart rate signal range. Steps 124and 126 repeat until the range is fine-tuned enough to discover theheart rate signal. When the heart rate signal is found, the method endswith step 128 where the estimated heart rate signal and the respirationrate signal are output for display and/or further analysis.

FIG. 13 is a flowchart illustrating another example of applying therepetitive bodily motion pattern search function. The method begins withstep 130 where biometrics of an individual at rest are obtained.Biometrics of the individual may include age, sex, size, and physicalcondition (e.g., athlete, sedentary lifestyle, moderately activelifestyle, etc.).

The method continues with step 132 where the expected range forrespiration rate and heart rate signals are determined based on theindividual's biometrics. The method continues with step 134 where thepattern search function is adjusted to search within the expectedranges. If the respiration rate data is still being collected, themethod continues to step 136 where the pattern search function isadjusted to search within the expected range for the heart rate. Becausethe frequency of respiration is less than that of the heart rate, it maytake a longer amount of time to obtain the necessary respiration samplesto estimate the respiration rate in comparison to the heart rate. Thus,time spent waiting for the respiration data to come in can be spentsearching for and honing in on the heart rate signal.

If the heart rate is not found after step 136, the method continues tostep 138 where the expected range for the heart rate is adjusted tobroaden the search. The method returns to the step of checking whetherthe respiration data has been collected. If the respiration data isstill being collected the method continues with step 136 where thepattern search function is adjusted to search within the new expectedrange for the heart rate. If the heart rate signal is found, the methodcontinues with step 140 where the heart rate signal is output fordisplay and/or further analysis. After the heart rate signal is foundand output at step 140, the method branches to the step of determiningwhether the respiration rate has been found. At this point, the knownheart rate data can be useful in determining the expected range of therespiration rate. The method would still be on hold here until therespiration data is collected.

If the respiration rate data has been collected, it is determinedwhether the heart rate has already been found. If the heart rate has notyet been found, the method continues with step 142 where the patternsearch function is adjusted to search within the expected ranges forrespiration rate and heart rate. If the signals are not found, themethod continues to step 144 where the expected ranges for therespiration rate and heart rates are adjusted to broaden the search.When the signals are found, the method continues to step 146 where theestimated respiration rate and heart rate are output for display and/orfurther analysis.

If the heart rate has been found after the respiration rate data hasbeen collected, the method continues to step 148 where the patternsearch function is adjusted to search within the expected range forrespiration rate based on the known heart rate information. For example,the respiration rate is typically between ½ to ⅕ that of the heart rate.For instance, if the heart rate is found to be 50 beats per minute, therespiration is most likely between 10 and 25 breaths per minute. Assuch, with the heart rate known, the range for the respiration rate canbe adjusted to search within a range based on the respiration rate.

If the respiration rate is still not found, the method continues withstep 150 where the expected respiration rate range is adjusted using theknown heart rate information to further broaden the search. The methodbranches back to step 148 where the pattern search function is adjustedbased on the new broadened parameters. When the respiration rate isfound the method continues with step 152 where the estimated respirationrate is output for display and/or further analysis.

FIG. 14 is a schematic block diagram of an embodiment of a processingmodule 22 of the computing device of the wireless monitoring repetitivebodily movement system. The processing module 22 includes an estimatecovariance matrix module 56, an estimate clutter covariance matrixmodule 58, an adder, a frequency estimation algorithm module 60, arepetitive bodily motion pattern search function module 62, and anaudible representation generator 154. The processing module 22 iscoupled to a speaker 156. The speaker 156 may be a separate device orintegrated into the computing device. Further, the speaker 156 may be aseparate wireless device with Bluetooth connection capabilities.

The estimate covariance matrix module 56, estimate clutter covariancematrix module 58, adder, frequency estimation algorithm module 60, andrepetitive bodily motion pattern search function module 62 operate aspreviously discussed in FIG. 22.

In an example of operation, the repetitive bodily motion pattern searchfunction 62 sends the estimated repetitive bodily motion rate data tothe audible representation generator 154. The audible representationgenerator 154 converts the data into an audible representation. Forexample, the audible representation generator 154 receives a signalrepresentative of an estimated heart rate of an individual. The audiblerepresentation generator 154 converts the estimated heart rate signalinto an audible representation of the heartbeat and sends it to thespeaker 156 for output as sound.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for determining a rate of repetitivebodily motion of an individual with negligible contact with theindividual, wherein the method is executable by one or more computingdevices and comprises: transmitting a signal for reflection off of theindividual; receiving a reflected signal; down-converting the reflectedsignal to a baseband signal; applying a frequency estimation algorithmto the baseband signal to produce an estimated spectral density; andapplying a repetitive bodily motion pattern search function to theestimated spectral density to estimate the rate of the repetitive bodilymotion of the individual.
 2. The method of claim 1 further comprises:the transmitting the signal for reflection off of the individual isperformed by a first computing device of the one or more computingdevices; the receiving the reflected signal is performed by the firstcomputing device of the one or more computing devices; thedown-converting the reflected signal to the baseband signal is performedby the first computing device of the one or more computing devices; theapplying the frequency estimation algorithm to the baseband signal toproduce the estimated spectral density is performed by a secondcomputing device of the one or more computing devices; and the applyingthe repetitive bodily motion pattern search function to the estimatedspectral density is performed by the second computing device of the oneor more computing devices.
 3. The method of claim 1 wherein thetransmitting the signal comprises: generating a continuous wavereference signal; up-converting the continuous wave reference signal toa radio frequency signal; and transmitting the radio frequency signal asthe signal.
 4. The method of claim 1 wherein the reflected signalcomprises one or more of: the transmitted signal; a clutter signal; amultipath signal; a noise component; and a Doppler shifted version ofthe repetitive bodily motion.
 5. The method of claim 1 wherein therepetitive bodily motion comprises at least one of: heartbeat;respiration; eye movement; spasms; and ticks.
 6. The method of claim 1wherein the applying the frequency estimation algorithm comprises:estimating a covariance matrix of the baseband signal; estimating aclutter covariance matrix of the baseband signal; generating a resultantmatrix from the clutter covariance matrix and the covariance matrix; andapplying the frequency estimation algorithm to the resultant matrix toproduce the estimated spectral density.
 7. The method of claim 1 whereinthe frequency estimation algorithm is a multiple signal classification(MUSIC) algorithm.
 8. The method of claim 1 wherein the applying therepetitive bodily motion pattern search function comprises: applying aheartbeat and respiration search function to the estimated spectraldensity to determine principal components of heartbeat and respiration,wherein the heartbeat and the respiration correspond to the repetitivebodily motion; and estimating a heart rate and a respiration rate fromthe determined principal components.
 9. The method of claim 8 furthercomprises: determining characteristics of the individual; and selectingthe heartbeat and respiration search function from a plurality ofheartbeat and respiration search functions based on the characteristicsof the individual.
 10. The method of claim 1 wherein the applying therepetitive bodily motion pattern search function comprises: applying aneye movement search function to the estimated spectral density todetermine principal components of eye movement, wherein the eye movementcorresponds to the repetitive bodily motion; and estimating an eyemovement rate from the determined principal components.
 11. The methodof claim 1 further comprises: outputting the rate of the repetitivebodily motion.
 12. The method of claim 1 further comprises: applying therepetitive bodily motion pattern search function to the estimatedspectral density to generate an audible representation of the repetitivebodily motion of the individual.
 13. A computing device comprises: atransceiver operable to: transmit a signal for reflection off of anindividual; receive a reflected signal; and down-convert the reflectedsignal to a baseband signal; and a memory; and a processing moduleoperably coupled to the transceiver and the memory, wherein theprocessing module is operable to: apply a frequency estimation algorithmto the baseband signal to produce an estimated spectral density; andapply a repetitive bodily motion pattern search function to theestimated spectral density to estimate the rate of the repetitive bodilymotion of the individual with negligible contact with the individual.14. The computing device of claim 13 wherein the transceiver furtherfunctions to transmit the signal by: generating a continuous wavereference signal; up-converting the continuous wave reference signal toa radio frequency signal; and transmitting the radio frequency signal asthe signal.
 15. The computing device of claim 13 wherein the reflectedsignal comprises one or more of: the transmitted signal; a cluttersignal; a multipath signal; a noise component; and a Doppler shiftedversion of the repetitive bodily motion.
 16. The computing device ofclaim 13 wherein the repetitive bodily motion comprises at least one of:heartbeat; respiration; eye movement; spasms; and ticks.
 17. Thecomputing device of claim 13 wherein the processing module furtherfunction to apply the frequency estimation algorithm by: estimating acovariance matrix of the baseband signal; estimating a cluttercovariance matrix of the baseband signal; generating a resultant matrixfrom the clutter covariance matrix and the covariance matrix; andapplying the frequency estimation algorithm to the resultant matrix toproduce the estimated spectral density.
 18. The computing device ofclaim 13 wherein the frequency estimation algorithm is a multiple signalclassification (MUSIC) algorithm.
 19. The computing device of claim 13wherein the processing module further functions to apply the repetitivebodily motion pattern search function by: applying a heartbeat andrespiration search function to the estimated spectral density todetermine principal components of heartbeat and respiration, wherein theheartbeat and the respiration correspond to the repetitive bodilymotion; and estimating a heart rate and a respiration rate from thedetermined principal components.
 20. The computing device of claim 19,wherein the processing module is further operable to: determinecharacteristics of the individual; and select the heartbeat andrespiration search function from a plurality of heartbeat andrespiration search functions based on the characteristics of theindividual.
 21. The computing device of claim 13 wherein the processingmodule further functions to apply the repetitive bodily motion patternsearch function by: applying an eye movement search function to theestimated spectral density to determine principal components of eyemovement, wherein the eye movement corresponds to the repetitive bodilymotion; and estimating an eye movement rate from the determinedprincipal components.
 22. The computing device of claim 13, wherein theprocessing module is further operable to: output the rate of therepetitive bodily motion.
 23. The computing device of claim 13, whereinthe processing module is further operable to: apply the repetitivebodily motion pattern search function to the estimated spectral densityto generate an audible representation of the repetitive bodily motion ofthe individual.